Update app.py

app.py

@@ -161,6 +161,411 @@ def voice_conversion(ta, ra, da):
 
   return (ap.sample_rate, ref_wav_voc)
 
+
+import os
+import sys
+
+os.system("git clone https://github.com/C0untFloyd/bark-gui.git")
+sys.path.append("./bark-gui/")
+
+from cProfile import label
+from distutils.command.check import check
+from doctest import Example
+import gradio as gr
+import numpy as np
+import logging
+import torch
+import pytorch_seed
+import time
+
+from xml.sax import saxutils
+from bark.api import generate_with_settings
+from bark.api import save_as_prompt
+from settings import Settings
+#import nltk
+
+from bark import SAMPLE_RATE
+from bark.clonevoice import clone_voice
+from bark.generation import SAMPLE_RATE, preload_models
+from scipy.io.wavfile import write as write_wav
+from parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml
+from datetime import datetime
+from tqdm.auto import tqdm
+from id3tagging import add_id3_tag
+
+OUTPUTFOLDER = "Outputs"
+
+
+def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, progress=gr.Progress(track_tqdm=True)):
+    if text == None or len(text) < 1:
+        raise gr.Error('No text entered!')
+
+    # Chunk the text into smaller pieces then combine the generated audio
+
+    # generation settings
+    if selected_speaker == 'None':
+        selected_speaker = None
+    if seed != None and seed > 2**32 - 1:
+        logger.warning(f"Seed {seed} > 2**32 - 1 (max), setting to random")
+        seed = None
+    if seed == None or seed <= 0:
+        seed = np.random.default_rng().integers(1, 2**32 - 1)
+    assert(0 < seed and seed < 2**32)
+
+    voice_name = selected_speaker
+    use_last_generation_as_history = "Use last generation as history" in complete_settings
+    save_last_generation = "Save generation as Voice" in complete_settings
+    progress(0, desc="Generating")
+
+    silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.float32)  # quarter second of silence
+    silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32)  # half a second of silence
+    full_generation = None
+
+    all_parts = []
+    complete_text = ""
+    text = text.lstrip()
+    if is_ssml(text):
+        list_speak = create_clips_from_ssml(text)
+        prev_speaker = None
+        for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):
+            selected_speaker = clip[0]
+            # Add pause break between speakers
+            if i > 0 and selected_speaker != prev_speaker:
+                all_parts += [silencelong.copy()]
+            prev_speaker = selected_speaker
+            text = clip[1]
+            text = saxutils.unescape(text)
+            if selected_speaker == "None":
+                selected_speaker = None
+
+            print(f"\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker} (Seed {seed}):`{text}`")
+            complete_text += text
+            with pytorch_seed.SavedRNG(seed):
+                audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
+                seed = torch.random.initial_seed()
+            if len(list_speak) > 1:
+                filename = create_filename(OUTPUTFOLDER, seed, "audioclip",".wav")
+                save_wav(audio_array, filename)
+                add_id3_tag(filename, text, selected_speaker, seed)
+
+            all_parts += [audio_array]
+    else:
+        texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length)
+        for i, text in tqdm(enumerate(texts), total=len(texts)):
+            print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {seed}):`{text}`")
+            complete_text += text
+            if quick_generation == True:
+                with pytorch_seed.SavedRNG(seed):
+                    audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
+                    seed = torch.random.initial_seed()
+            else:
+                full_output = use_last_generation_as_history or save_last_generation
+                if full_output:
+                    full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True)
+                else:
+                    audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
+
+            # Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format  
+            # audio_array = (audio_array * 32767).astype(np.int16)
+
+            if len(texts) > 1:
+                filename = create_filename(OUTPUTFOLDER, seed, "audioclip",".wav")
+                save_wav(audio_array, filename)
+                add_id3_tag(filename, text, selected_speaker, seed)
+
+            if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True):
+                # save to npz
+                voice_name = create_filename(OUTPUTFOLDER, seed, "audioclip", ".npz")
+                save_as_prompt(voice_name, full_generation)
+                if use_last_generation_as_history:
+                    selected_speaker = voice_name
+
+            all_parts += [audio_array]
+            # Add short pause between sentences
+            if text[-1] in "!?.\n" and i > 1:
+                all_parts += [silenceshort.copy()]
+
+    # save & play audio
+    result = create_filename(OUTPUTFOLDER, seed, "final",".wav")
+    save_wav(np.concatenate(all_parts), result)
+    # write id3 tag with text truncated to 60 chars, as a precaution...
+    add_id3_tag(result, complete_text, selected_speaker, seed)
+    return result
+
+def create_filename(path, seed, name, extension):
+    now = datetime.now()
+    date_str =now.strftime("%m-%d-%Y")
+    outputs_folder = os.path.join(os.getcwd(), path)
+    if not os.path.exists(outputs_folder):
+        os.makedirs(outputs_folder)
+
+    sub_folder = os.path.join(outputs_folder, date_str)
+    if not os.path.exists(sub_folder):
+        os.makedirs(sub_folder)
+
+    time_str = now.strftime("%H-%M-%S")
+    file_name = f"{name}_{time_str}_s{seed}{extension}"
+    return os.path.join(sub_folder, file_name)
+
+
+def save_wav(audio_array, filename):
+    write_wav(filename, SAMPLE_RATE, audio_array)
+
+def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):
+    np.savez_compressed(
+        filename,
+        semantic_prompt=semantic_prompt,
+        coarse_prompt=coarse_prompt,
+        fine_prompt=fine_prompt
+    )
+    
+
+def on_quick_gen_changed(checkbox):
+    if checkbox == False:
+        return gr.CheckboxGroup.update(visible=True)
+    return gr.CheckboxGroup.update(visible=False)
+
+def delete_output_files(checkbox_state):
+    if checkbox_state:
+        outputs_folder = os.path.join(os.getcwd(), OUTPUTFOLDER)
+        if os.path.exists(outputs_folder):
+            purgedir(outputs_folder)
+    return False
+
+
+# https://stackoverflow.com/a/54494779
+def purgedir(parent):
+    for root, dirs, files in os.walk(parent):                                      
+        for item in files:
+            # Delete subordinate files                                                 
+            filespec = os.path.join(root, item)
+            os.unlink(filespec)
+        for item in dirs:
+            # Recursively perform this operation for subordinate directories   
+            purgedir(os.path.join(root, item))
+
+def convert_text_to_ssml(text, selected_speaker):
+    return build_ssml(text, selected_speaker)
+
+
+def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker):
+    settings.selected_theme = themes
+    settings.server_name = input_server_name
+    settings.server_port = input_server_port
+    settings.server_share = input_server_public
+    settings.input_text_desired_length = input_desired_len
+    settings.input_text_max_length = input_max_len
+    settings.silence_sentence = input_silence_break
+    settings.silence_speaker = input_silence_speaker
+    settings.save()
+
+def restart():
+    global restart_server
+    restart_server = True
+
+
+def create_version_html():
+    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
+    versions_html = f"""
+python: <span title="{sys.version}">{python_version}</span>
+ • 
+torch: {getattr(torch, '__long_version__',torch.__version__)}
+ • 
+gradio: {gr.__version__}
+"""
+    return versions_html
+
+    
+
+logger = logging.getLogger(__name__)
+APPTITLE = "Bark UI Enhanced v0.4.6"
+
+
+autolaunch = False
+
+if len(sys.argv) > 1:
+    autolaunch = "-autolaunch" in sys.argv
+
+
+if torch.cuda.is_available() == False:
+    os.environ['BARK_FORCE_CPU'] = 'True'
+    logger.warning("No CUDA detected, fallback to CPU!")
+
+print(f'smallmodels={os.environ.get("SUNO_USE_SMALL_MODELS", False)}')
+print(f'enablemps={os.environ.get("SUNO_ENABLE_MPS", False)}')
+print(f'offloadcpu={os.environ.get("SUNO_OFFLOAD_CPU", False)}')
+print(f'forcecpu={os.environ.get("BARK_FORCE_CPU", False)}')
+print(f'autolaunch={autolaunch}\n\n')
+
+#print("Updating nltk\n")
+#nltk.download('punkt')
+
+print("Preloading Models\n")
+preload_models()
+
+settings = Settings('config.yaml')
+
+# Collect all existing speakers/voices in dir
+speakers_list = []
+
+for root, dirs, files in os.walk("./bark/assets/prompts"):
+	for file in files:
+		if(file.endswith(".npz")):
+			pathpart = root.replace("./bark/assets/prompts", "")
+			name = os.path.join(pathpart, file[:-4])
+			if name.startswith("/") or name.startswith("\\"):
+				name = name[1:]
+			speakers_list.append(name)
+
+speakers_list = sorted(speakers_list, key=lambda x: x.lower())
+speakers_list.insert(0, 'None')
+
+available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"]
+
+seed = -1
+server_name = settings.server_name
+if len(server_name) < 1:
+    server_name = None
+server_port = settings.server_port
+if server_port <= 0:
+    server_port = None
+global run_server
+global restart_server
+
+run_server = True
+
+
+while run_server:
+    print(f'Launching {APPTITLE} Server')
+
+    # Create Gradio Blocks
+
+    with gr.Blocks(title=f"{APPTITLE}", mode=f"{APPTITLE}", theme=settings.selected_theme) as barkgui:
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown(f"### [{APPTITLE}](https://github.com/C0untFloyd/bark-gui)")
+            with gr.Column():
+                gr.HTML(create_version_html(), elem_id="versions")
+
+        with gr.Tab("TTS"):
+            with gr.Row():
+                with gr.Column():
+                    placeholder = "Enter text here."
+                    input_text = gr.Textbox(label="Input Text", lines=4, placeholder=placeholder)
+                with gr.Column():
+                    seedcomponent = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
+                    convert_to_ssml_button = gr.Button("Convert Text to SSML")
+            with gr.Row():
+                with gr.Column():
+                    examples = [
+                        "Special meanings: [laughter] [laughs] [sighs] [music] [gasps] [clears throat] MAN: WOMAN:",
+                       "♪ Never gonna make you cry, never gonna say goodbye, never gonna tell a lie and hurt you ♪",
+                       "And now — a picture of a larch [laughter]",
+                       """
+                            WOMAN: I would like an oatmilk latte please.
+                            MAN: Wow, that's expensive!
+                       """,
+                       """<?xml version="1.0"?>
+    <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
+             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+             xsi:schemaLocation="http://www.w3.org/2001/10/synthesis
+                       http://www.w3.org/TR/speech-synthesis/synthesis.xsd"
+             xml:lang="en-US">
+    <voice name="en_speaker_9">Look at that drunk guy!</voice>
+    <voice name="en_speaker_3">Who is he?</voice>
+    <voice name="en_speaker_9">WOMAN: [clears throat] 10 years ago, he proposed me and I rejected him.</voice>
+    <voice name="en_speaker_3">Oh my God [laughs] he is still celebrating</voice>
+    </speak>"""
+                       ]
+                    examples = gr.Examples(examples=examples, inputs=input_text)
+
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("[Voice Prompt Library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)")
+                    speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice")
+                with gr.Column():
+                    text_temp = gr.Slider(0.1, 1.0, value=0.6, label="Generation Temperature", info="1.0 more diverse, 0.1 more conservative")
+                    waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label="Waveform temperature", info="1.0 more diverse, 0.1 more conservative")
+
+            with gr.Row():
+                with gr.Column():
+                    quick_gen_checkbox = gr.Checkbox(label="Quick Generation", value=True)
+                    settings_checkboxes = ["Use last generation as history", "Save generation as Voice"]
+                    complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
+                with gr.Column():
+                    eos_prob = gr.Slider(0.0, 0.5, value=0.05, label="End of sentence probability")
+
+            with gr.Row():
+                with gr.Column():
+                    tts_create_button = gr.Button("Generate")
+                with gr.Column():
+                    hidden_checkbox = gr.Checkbox(visible=False)
+                    button_stop_generation = gr.Button("Stop generation")
+            with gr.Row():
+                output_audio = gr.Audio(label="Generated Audio", type="filepath")
+
+            with gr.Row():
+                inp1 = gr.Audio(label='Target Speaker - Reference Clip')
+                inp2 = gr.Audio(label='Input Speaker - Reference Clip')
+                inp3 = gr.Audio(label='Input Speaker - Clip To Convert')
+                btn = gr.Button("Generate")
+                out1 = gr.Audio(label='Target Speaker - Converted Clip')
+            btn.click(voice_conversion, [inp1, inp2, inp3], [out1])
+                
+
+        with gr.Tab("Clone Voice"):
+            input_audio_filename = gr.Audio(label="Input audio.wav", source="upload", type="filepath")
+            transcription_text = gr.Textbox(label="Transcription Text", lines=1, placeholder="Enter Text of your Audio Sample here...")
+            initialname = "./bark/assets/prompts/custom/MeMyselfAndI"
+            output_voice = gr.Textbox(label="Filename of trained Voice", lines=1, placeholder=initialname, value=initialname)
+            clone_voice_button = gr.Button("Create Voice")
+            dummy = gr.Text(label="Progress")
+
+        with gr.Tab("Settings"):
+            with gr.Row():
+                themes = gr.Dropdown(available_themes, label="Theme", info="Change needs complete restart", value=settings.selected_theme)
+            with gr.Row():
+                input_server_name = gr.Textbox(label="Server Name", lines=1, info="Leave blank to run locally", value=settings.server_name)
+                input_server_port = gr.Number(label="Server Port", precision=0, info="Leave at 0 to use default", value=settings.server_port)
+                share_checkbox = gr.Checkbox(label="Public Server", value=settings.server_share)
+            with gr.Row():
+                input_desired_len = gr.Slider(100, 150, value=settings.input_text_desired_length, label="Desired Input Text Length", info="Ideal length to split input sentences")
+                input_max_len = gr.Slider(150, 256, value=settings.input_text_max_length, label="Max Input Text Length", info="Maximum Input Text Length")
+            with gr.Row():
+                input_silence_break = gr.Slider(1, 1000, value=settings.silence_sentence, label="Sentence Pause Time (ms)", info="Silence between sentences in milliseconds")
+                input_silence_speakers = gr.Slider(1, 5000, value=settings.silence_speakers, label="Speaker Pause Time (ms)", info="Silence between different speakers in milliseconds")
+
+            with gr.Row():
+                button_apply_settings = gr.Button("Apply Settings")
+                button_apply_restart = gr.Button("Restart Server")
+                button_delete_files = gr.Button("Clear output folder")
+
+        quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
+        convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
+        gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent],outputs=output_audio)
+        button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click])
+        # Javascript hack to display modal confirmation dialog
+        js = "(x) => confirm('Are you sure? This will remove all files from output folder')"
+        button_delete_files.click(None, None, hidden_checkbox, _js=js)
+        hidden_checkbox.change(delete_output_files, [hidden_checkbox], [hidden_checkbox])
+        clone_voice_button.click(clone_voice, inputs=[input_audio_filename, transcription_text, output_voice], outputs=dummy)
+        button_apply_settings.click(apply_settings, inputs=[themes, input_server_name, input_server_port, share_checkbox, input_desired_len, input_max_len, input_silence_break, input_silence_speakers])
+        button_apply_restart.click(restart)
+        restart_server = False
+        try:
+            barkgui.queue().launch(show_error=True)
+        except:
+            restart_server = True
+            run_server = False
+        try:
+            while restart_server == False:
+                time.sleep(1.0)
+        except (KeyboardInterrupt, OSError):
+            print("Keyboard interruption in main thread... closing server.")
+            run_server = False
+        barkgui.close()
+
+
 c3 = gr.Interface(
     fn=voice_conversion,
     inputs=[gr.Audio(label='Target Speaker - Reference Clip'), gr.Audio(label='Input Speaker - Reference Clip'), gr.Audio(label='Input Speaker - Clip To Convert')],